1. Technical Field
This invention is related to the field of computational modeling of stochastic processes, and more particularly to techniques for efficiently performing goal-driven searches of stochastic processes.
2. Description of the Related Art
Many types of physical phenomena are not deterministic. For example, when manufacturing semiconductor circuits, it may be impossible to guarantee that devices within different regions of a substrate have identical physical or electrical characteristics. Instead, the various characteristics of different devices may exhibit random variation. In the face of such variation, it may not be possible to predict how any given device will behave with certainty.
However, it may be possible to predict how such random or stochastic processes may behave within some degree of statistical confidence. For example, random processes may conform to a particular probability distribution. If a sufficient number of data points are generated according to the relevant probability distribution, it may be possible to determine whether the process will conform to a particular expectation with a particular level of confidence (e.g., that at least 99.9% of finished parts will have fewer than some threshold number N of randomly-distributed defects).
Computer-based simulations may facilitate the statistical characterization of a stochastic process. For example, a computer may repeatedly evaluate a model of the process to generate a number of discrete observations drawn from the probability distribution that models the process. However, such modeling can demand significant amounts of computing power, time, or both in order to yield useful results. For example, in order to ensure that the probability distribution is reasonably well sampled, it may be necessary to repeat the modeling process hundreds or thousands of times.